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This paper introduces the Fusemate probabilistic logic programming system.Fusemate's inference engine comprises a grounding component and a variableelimination method for probabilistic inference. Fusemate differs from mostother systems by grounding the program in a bottom-up way instead of the commontop-down way. While bottom-up grounding is attractive for a number of reasons,e.g., for dynamically creating distributions of varying support sizes, it makesit harder to control the amount of ground clauses generated. We address thisproblem by interleaving grounding with a query-guided relevance test whichprunes rules whose bodies are inconsistent with the query. We present ourmethod in detail and demonstrate it with examples that involve "time", such as(hidden) Markov models. Our experiments demonstrate competitive or betterperformance compared to a state-of-the art probabilistic logic programmingsystem, in particular for high branching problems.

Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate